In silico prediction of SARS-CoV-2 epitopes for vaccine development

Abstract

The ongoing coronavirus disease (COVID-19) pandemic, caused by severe acute respiratory syndrome
coronavirus 2 (SARS-CoV-2), is causing major damages in health and economies worldwide. The development of safe and effective vaccines for COVID-19 is of utmost importance yet none have been licensed to date. One of the strategies for vaccine development utilizes dendritic cells which express class I and class II human leukocyte antigen (HLA) molecules. These HLA molecules present the antigenic peptides to T cells which mediate the immune response. Thus, the study aimed to identify SARS-CoV-2 peptides with potential binding to HLA class I and class II molecules using different bioinformatics tools. SYFPEITHI and IEDB were used to predict epitopes for the most common HLA class I and II alleles among Filipinos. The top predicted epitopes were subjected to de novo and template-based molecular docking. Then, binding energies of the generated peptide-HLA complexes to putative T cell receptors were predicted using a homology modeling approach. Several predicted epitopes showed promising MHC and TCR binding, although results varied considerably between the prediction methods used. In particular, the results of de novo and template-based docking methods did not coincide, the latter of which generated complexes that more closely resemble typical peptide-HLA complexes. The results of this study will be validated by the next stage of the vaccine development project which is the in vitro assessment of the T cell responses elicited by dendritic cells pulsed with the candidate peptides.